Article citationsMore >>

Yang F, Jin S, Li Z. A comprehensive study of linear variation propagation modeling methods for multistage machining processes. Int J Adv Manuf Technol, 2017, 90: 2139-2151.

has been cited by the following article:

Article

Geometric Deviation Representation and Variation Propagation Modeling in Multistage Machining Processes

1School of Intelligent Manufacturing, Jianghan University, Wuhan 430056, China


American Journal of Mechanical Engineering. 2023, Vol. 11 No. 4, 166-174
DOI: 10.12691/ajme-11-4-6
Copyright © 2023 Science and Education Publishing

Cite this paper:
Heping Peng, Qianpeng Han. Geometric Deviation Representation and Variation Propagation Modeling in Multistage Machining Processes. American Journal of Mechanical Engineering. 2023; 11(4):166-174. doi: 10.12691/ajme-11-4-6.

Correspondence to: Heping  Peng, School of Intelligent Manufacturing, Jianghan University, Wuhan 430056, China. Email: hbjhun_penn@outlook.com

Abstract

The development of 3D manufacturing variation propagation model for multistage machining processes (MMPs) plays an important role for estimating the dimensional and geometric quality of machined part, monitoring the machining processes and diagnosing the variation sources, and realizing process planning evaluation and selection. In this paper, after the error sources and their propagation modes in the part machining processes are analyzed, the differential motion vectors are employed to describe locating datum-, fixture-, and machining-induced variations, a series of coordinate transformations of variation sources are used to realize the deviation accumulation and transformation, and the mathematical relationship between the key product characteristics and various machining process parameters is established, so as to realize the quantitative analysis of error propagation, error accumulation and coupling, and establish the linear explicit Stream of Variation model (SoV) of multistage machining processes. The establishment of the model reveals the dynamic evolution law of the generation, coupling and propagation of product error flow and provides effective scientific guidance for the optimal control, system design, and error source diagnosis of MMPs. The effectiveness of the proposed model is verified by a case study.

Keywords